In a localization system such as an indoor positioning system, the location of a wireless device such as a mobile user terminal can be determined with respect to a location network comprising a plurality of wireless reference nodes, in some cases also referred to as anchor nodes. These anchors are wireless nodes whose locations are known a priori, typically being recorded in a location database which can be queried to look up the location of a node. The anchor nodes thus act as reference nodes for localization. Measurements are taken of the signals transmitted between the mobile device and a plurality of anchor nodes, for instance the RSSI (receiver signal strength indicator), ToA (time of arrival) and/or AoA (angle of arrival) of the respective signal. Given such a measurement from three or more nodes, the location of the mobile terminal may then be determined relative to the location network using techniques such as trilateration, multilateration or triangulation. Given the relative location of the mobile terminal and the known locations of the anchor nodes, this in turn allows the location of the mobile device to be determined in more absolute terms, e.g. relative to the globe or a map or floorplan.
Another localization technique is to determine the location of mobile device based on a “fingerprint” of a known environment. The fingerprint comprises a set of data points each corresponding to a respective one of a plurality of locations throughout the environment in question. Each data point is generated during a training phase by placing a wireless device at the respective location, taking a measurement of the signals received from or by any reference nodes within range at the respective location (e.g. a measure of signal strength such as RSSI), and storing these measurements in a location server along with the coordinates of the respective location. The data point is stored along with other such data points in order to build up a fingerprint of the signal measurements as experienced at various locations within the environment. Once deployed, the signal measurements stored in the fingerprint can then be compared with signal measurements currently experienced by a mobile device whose location is desired to be known, in order to estimate the location of the mobile device relative to the corresponding coordinates of the points in the fingerprint. For example this may be done by approximating that the device is located at the coordinates of the data point having the closest matching signal measurements, or by interpolating between the coordinates of a subset of the data points having signal measurements most closely matching those currently experienced by the device. The fingerprint can be pre-trained in a dedicated training phase before the fingerprint is deployed by systematically placing a test device at various different locations in the environment. Alternatively or additionally, the fingerprint can built up dynamically by receiving submissions of signal measurements experienced by the actual devices of actual users in an ongoing training phase.
The determination of the mobile device's location may be performed according to a “device-centric” approach or a “network-centric” approach. According to a device centric approach, each anchor or reference node emits a respective beacon signal. The mobile device takes measurements of beacon signals it receives from the reference nodes, obtains the locations of those nodes from the location server, and performs the calculation to determine its own location at the mobile device itself. According to a network-centric approach on the other hand, the reference nodes are used to take measurements of beacon signals received from the mobile device, and an element of the network such as the location server performs the calculation to determine the mobile device's location. Hybrid approaches are also possible, e.g. where the mobile device takes the raw measurements but forwards them to the location server to calculate its location (also sometimes referred to as an “assisted” approach).
There are various reasons why it may be desirable to be able to detect the location of a wireless device, such as to provide location based services. For instance, one application of a positioning system is to automatically provide a wireless mobile device with access to control of a utility such as a lighting system, on condition that the mobile device is found to be located in a particular spatial region or zone associated with the lighting or other utility. E.g. access to control of the lighting in a room may be provided to a wireless user device on condition that the device is found to be located within that room and requests access. Once a wireless user device has been located and determined to be within a valid region, control access is provided to that device via a lighting control network. Other examples of location based services or functionality include indoor navigation, location-based advertising, service alerts or provision of other location-related information, user tracking, asset tracking, or taking payment of road tolls or other location dependent payments.
It is also known to incorporate the beaconing functionality of an anchor node into another unit that is designed to provide another utility such as lighting into the environment in question, rather than the anchor node being a separate, dedicated, stand-alone unit. Consider for example a smart lighting system with wireless lighting control and radio-based indoor positioning. Here each node of the positioning system is also a luminaire for illuminating the environment.
There is a trend toward having greater connectivity and intelligence in lighting systems. Wireless RF-networked lighting systems will play an important role with the desire for easier commissioning and control of lighting systems. A resulting consequence will be a dense deployment of wireless nodes, e.g. one wireless anchor node per luminaire or a wireless anchor node for a group of luminaires in a room. A dense deployment of wireless anchor nodes has been considered in WO2014/083494. The technique described in WO2014/083494 uses “RF zoning”, whereby instead of trying to compute the actual point position of the device using techniques such as triangulation, trilateration or multilateration, instead the anchor nodes are divided into groups corresponding to discrete zones and the location is determined in terms of which zone the mobile device is estimated to be found in, depending on which zone experiences the highest average signal strength. For a dense deployment of nodes, this can be less computationally complex than computing the position using techniques such as trilateration or multilateration, and/or less onerous in terms of commissioning.
Further, existing triangulation, trilateration or multilateration techniques based on only an instantaneous set of radio beacon measurements suffer from the intrinsic randomness in the radio signal measurements. For instance, even when a mobile user device is static, the estimated position may fluctuate due to random variations in the RSSI measurements (or the like). Hence as well as being spatially averaged, in the RF zoning technique of WO2014/083494 the signals are also temporally averaged in order to try to minimize the effect of short-term fluctuations.